Basic description of this class

• Lecturer: Zhiguang Huo (Caleb) zhuo@ufl.edu
• TAs: Xiulin Xie xiulin.xie@ufl.edu
• Lecture times:
  • Monday: 12:50-2:45pm
  • Wednesday: 1:55-2:45pm
  • Check out canvas website https://elearning.ufl.edu/ for the Zoom link
  • All lectures will be recorded, and the recorded videos will be made available
• Office hours:
  • Caleb: Monday 3:00-5:00pm
  • Xiulin: Wednesday 3:00-5:00pm
  • Zoom (https://ufl.zoom.us/j/98744965403)
• Course Website:

https://caleb-huo.github.io/teaching/2020FALL/biostatisticalComputing.html

• elearning: used to collect submissions, and keep track of grades
• Syllabus: will be explained in the following slides.

More description of this class

• Major targeted audience:
  • First/Second year PhD students in Biostatistics department.
  • Second year Master’s students in Biostatistics department.
• No programming knowledge assumed
• Some statistics knowledge assumed
  • PHC 6092: Biostatistical Theory
  • PHC 6050: Statistical Methods for Health Sciences Research I
  • PHC 6051: Biostatistical Methods II
• Focus almost entirely on R
  • R usage
  • Many statistical model applications
  • Some algorithm implementations
• Class will be cumulative, so keep up with the material and assignments!

Contents of this class

• See syllabus https://caleb-huo.github.io/teaching/2020FALL/PHC6068_2020_Biostatistical_Computing_Syllabus.pdf
• Lecture notes https://caleb-huo.github.io/teaching/2020FALL/biostatisticalComputing.html
  • Former part: how to use R.
  • Later part: how to perform statistical modeling using R.
• Homework assignment, Exams, Final projects and grades will be available on elearning

Textbook

• No textbook is required
• If you never used R before
  • Warm up here: https://www.datacamp.com/courses/free-introduction-to-r
• Recommanded reading:
  • Google’s R Style Guide. https://google.github.io/styleguide/Rguide.xml
  • Hadley Wickham (2014). Advanced R. Taylor & Francis. http://adv-r.had.co.nz
  • Garrett Grolemund and Hadley Wickham (2016). R for Data Science. O’Reilly Media, Inc., https://r4ds.had.co.nz/

Assignment

Homework policy

• Format: will be released/submitted in R markdown.
  • Will introduce later in today’s class.
• Due time:
  • Two weeks after the homework is released, usually due on Monday 11:59 pm.
• Late homework:
  • 80% credit within one day late;
  • 50% credit within two day late;
  • 0% after two day’s late.
• Grace period: totally 2 days.
  • Can be applied to any homework (not exam or final project).
  • Each time at least one day of grace period will be used.
• Where to submit: electronically via elearning.
• Can I work with other people?
  • Yes. You are allowed to discuss how to do the Homework with your classmates.
  • No. But you have to write your own solution.

Trouble shooting

• You may ask questions during the live zoom class.
• You may ask questions during office hours.
• You may ask/answer questions in the canvas discussion section.

Exams

• Mid-term exam:
  • 1 week take-home exam.
• Final exam:
  • 1 week take-home exam.
• Format:
  • will be released/submitted in R markdown.
• Due time:
  • See course website, no late exam submissions.
• Can I work with other people?
  • No, you are not allowed to discuss with other people since this is exam.

Final project

• Wrap up a R package
  • Complete documentation of all your functions.
  • Apply R package to real data or synthetic data.
  • Report using R markdown, include how to use the package.
  • Pass all warning and error checks.
• How to make an outstanding R package?
  • Calling c++
  • Make nice vignette
  • A comprehensive package
  • Host the package on GitHub
• Instructions: will be released on Monday Sep 28.
• Can work alone, or work in group with less than 3.

Any question about administration?

What software do we need?

• R is a programming language for statistical computing
• R Studio is an integrated development environment for R programming
  
• R Markdown is a markup language for combining R code with text

All 3 are free, and all 3 will be used extensively in this course

Installing R

• R can be downloaded from the ``Comprehensive R Archive Network" (CRAN).
• R can be installed on Windows, Mac or Linux operating systems.
• In this course, the instructors will run R on Mac/Windows.
• To install R, visit the R website, http://www.r-project.org and follow the installation directions (see the FAQ section for more details).

R Console

• When you first open R, you will see the R window. The console is the fundamental way for interacting with R.
• In the console is a prompt, > this is where an expression, phrase of code that can be executed, is inputed into R (if entering lengthy expressions that require more than one line the prompt changes from > to +.
• An expression is typed into the console at the prompt, press Enter, and the expression is evaluated.
• Depending on the expression, the system may respond by outputting results to the console, creating a graph in a new window or performing some other task. Then another expression can be inputed and evaluated.
• An R session is this interaction between you and the system.
• Often included with outputted results in the console are numbers in [ ]. These numbers are used to reference locations in the output.

RStudio

• RStudio is a free and open source integrated development environment (IDE) designed specifically for R. http://www.rstudio.com
• Runs on, Windows, Mac or Linux operating systems.
• Useful productivity tools：
  • Syntax highlighting, smart indention, parentheses matching
  • Easily execute code from the source editor
  • Integrates all of the tools (source editor, console, plots, documentation, and more) when working with R into a single environment
  • Extensive tools for developing R packages
  • Interactive debugging for diagnosing and fixing errors

R markdown

Will be introduced later today

HiPerGator

• HiPerGator is high performance computing for UF.
• We will use HiPerGator for jobs with heavy computation/large memory.
• You have to agree with following Acceptable Use Policy:
  • ACCEPTABLE USE I acknowledge that the access to the HPC resources operated by UF Research Computing is subject to the UF Acceptable Use Policy at https://it.ufl.edu/policies/acceptable-use/acceptable-use-policy/ and the Research Computing policies at http://www.rc.ufl.edu/services/procedures/ and that I am responsible for following these policies.
  • RESTRICTED DATA I also certify that using restricted data and software on the HPC resources requires extra steps described at UFRC Policies and at UFRC Export Policies, and that I will notify both my account sponsor and the Office of Research (Research Compliance) and Research Computing at support.rc.ufl.edu when I am working with such data.

Advantages of R

• It’s free, open source, and available on every major platform.
• A massive set of packges for statistical modeling, machine learning, visualization and importing and manipulating data.
• Cutting edge tools.
• A fantastic community.
• Powerful tools for communicating your results. (html, pdf, interactive website).
• An IDE (RStudio).
• Connect to high-performance programming languages (C, C++, Java)
• When you published a statistical methodology paper, often you will be asked to publish a R package as well.

Some drawbacks of R

• R is not a particularly fast programming language, and poorly written R code can be terribly slow. R is also a profligate user of memory.
• R language focuses on results instead of processes. Too many R functions use tricks to reduce the amount of typing at the cost of making code hard to understand.
• R is slow in loop (e.g. for, while) so try to avoid loop in R if you run a large program and care about speed.
  • Solution: vectorize your program.

Things you should have done, by the end of this week

• Install R and R Studio on your laptop
• Get comfortable with R Studio, knitting Rmd documents into HTML files
• Get comfortable with navigating R help files
• Get comfortable with installing R packages
  • below is an example how to install a R package

  install.package("mclust")